We compile citations and summaries of about 400 new articles every week.
Email Signup | RSS Feed

HELP: Tutorials | FAQ
CONTACT US: Contact info

Search Results

Journal Article


Eckerstorfer M, Malnes E, Muller K. Cold Reg. Sci. Technol. 2017; 144: 39-51.


(Copyright © 2017, Elsevier Publishing)






Collecting a complete dataset of snow avalanche activity in a given avalanche forecasting region throughout an entire winter is a seemingly simple task that is very difficult to accomplish. Traditional field-based methods are too time and cost consuming and limited to good visibility and accessible terrain. Different remote sensing methods have been proposed to assist in monitoring of snow avalanche activity, with satellite-borne radar remote sensing allowing for all-weather and all-light conditions monitoring. Here we present a complete, two-year snow avalanche activity record from a Norwegian forecasting region using Sentinel-1A radar satellite imagery. We used a change detection method to manually identify avalanche debris based on localized backscatter increase in avalanche runout zones and assess these detections using multi-temporal and multi-sensor datasets. We then used the complete dataset of snow avalanche activity to exemplify different applications from validating issued avalanche bulletins, to generating avalanche path maps, and creating a snow climate classification. Comparing manual identification in Sentinel-1A images and a very high resolution Radarsat-2 image suggest overall underestimation of avalanche activity, also when considering the non-detectability of small avalanche debris. We foresee that with the further increase in satellite-radar data availability and the improvement of automatized snow avalanche detection, the presented methodologies will be able to assist in operational avalanche forecasting worldwide in the near future.

Language: en


Avalanche forecasting; Remote sensing; SAR; Sentinel-1; Snow avalanche activity; Snow avalanche detection


All SafetyLit records are available for automatic download to Zotero & Mendeley